mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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88 lines
3.4 KiB
C++
88 lines
3.4 KiB
C++
/*
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* SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: Apache-2.0
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "tensorrt_llm/batch_manager/allocateKvCache.h"
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#include "tensorrt_llm/common/logger.h"
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#include "tensorrt_llm/common/nvtxUtils.h"
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void tensorrt_llm::batch_manager::AllocateKvCache::operator()(BaseKVCacheManager& kvCacheManager,
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RequestVector& contextRequests, RequestVector const& generationRequests, runtime::ModelConfig const& modelConfig,
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OptionalRef<BaseKVCacheManager> crossKvCacheManager) const
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{
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TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
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NVTX3_SCOPED_RANGE(allocateKvCache);
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for (auto const& llmReq : contextRequests)
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{
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if (llmReq->isFirstContextChunk())
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{
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auto const requestId = llmReq->mRequestId;
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auto const promptLen = llmReq->mPromptLen;
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auto const reqBeamWidth = llmReq->mSamplingConfig.beamWidth;
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auto draftLength = llmReq->getNumDraftTokens();
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// Allocate/Reuse KV cache
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kvCacheManager.addSequence(requestId, promptLen, reqBeamWidth, llmReq);
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// EagleNet will increment kv cache up to maxPathLen to account for accepted tokens.
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// Then up to maxDecodingDraftTokens will be used to generate next draft tokens.
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if (modelConfig.getSpeculativeDecodingMode().isEagle())
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{
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draftLength = modelConfig.getSpeculativeDecodingModule().getMaxPathLen()
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+ modelConfig.getSpeculativeDecodingModule().getMaxDecodingTokens();
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}
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// Allocate more KV cache for speculative decoding
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if (draftLength > 0)
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{
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for (SizeType32 di = 0; di < draftLength; ++di)
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{
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kvCacheManager.addToken(requestId);
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}
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}
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if (crossKvCacheManager)
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{
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crossKvCacheManager->addSequence(requestId, llmReq->getEncoderOutputLen(), reqBeamWidth, llmReq);
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}
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}
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}
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for (auto const& llmReq : generationRequests)
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{
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auto const requestId = llmReq->mRequestId;
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auto decodingTokens = llmReq->getNumDraftTokens() + 1;
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// EagleNet will increment kv cache up to maxPathLen to account for accepted tokens.
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// Then up to maxDecodingDraftTokens will be used to generate next draft tokens.
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if (modelConfig.getSpeculativeDecodingMode().isEagle())
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{
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decodingTokens = modelConfig.getSpeculativeDecodingModule().getMaxPathLen()
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+ modelConfig.getSpeculativeDecodingModule().getMaxDecodingTokens();
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}
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for (SizeType32 di = 0; di < decodingTokens; ++di)
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{
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kvCacheManager.addToken(requestId);
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}
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}
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kvCacheManager.refreshBlocks();
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TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
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}
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